#!/usr/bin/python3
import cv2
import rospy
from yolo_pkg.msg import Yolo
import numpy as np
#初始化节点
rospy.init_node("huoyuandetect", anonymous=True)
pub = rospy.Publisher("/huoyuandetect", Yolo, queue_size=10)
rate = rospy.Rate(100)
import os
os.system("gpio mode 12 out")
os.system("gpio write 12 1")
from rknnlite.api import RKNNLite



RKNN_MODEL = r'/home/orangepi/catkin_ws/src/yolo_pkg/rknn/huoyuan.rknn'
 
QUANTIZE_ON = True
 
OBJ_THRESH = 0.75
NMS_THRESH = 0.2
IMG_SIZE = 640
 
CLASSES = ("0", "1")
 
 
def sigmoid(x):
    return 1 / (1 + np.exp(-x))
 
 
def xywh2xyxy(x):
    # Convert [x, y, w, h] to [x1, y1, x2, y2]
    y = np.copy(x)
    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
    return y
 
 
def process(input, mask, anchors):
 
    anchors = [anchors[i] for i in mask]
    grid_h, grid_w = map(int, input.shape[0:2])
 
    box_confidence = sigmoid(input[..., 4])
    box_confidence = np.expand_dims(box_confidence, axis=-1)
 
    box_class_probs = sigmoid(input[..., 5:])
 
    box_xy = sigmoid(input[..., :2])*2 - 0.5
 
    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    grid = np.concatenate((col, row), axis=-1)
    box_xy += grid
    box_xy *= int(IMG_SIZE/grid_h)
 
    box_wh = pow(sigmoid(input[..., 2:4])*2, 2)
    box_wh = box_wh * anchors
 
    box = np.concatenate((box_xy, box_wh), axis=-1)
 
    return box, box_confidence, box_class_probs
 
 
def filter_boxes(boxes, box_confidences, box_class_probs):
    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
    # Arguments
        boxes: ndarray, boxes of objects.
        box_confidences: ndarray, confidences of objects.
        box_class_probs: ndarray, class_probs of objects.
    # Returns
        boxes: ndarray, filtered boxes.
        classes: ndarray, classes for boxes.
        scores: ndarray, scores for boxes.
    """
    boxes = boxes.reshape(-1, 4)
    box_confidences = box_confidences.reshape(-1)
    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
 
    _box_pos = np.where(box_confidences >= OBJ_THRESH)
    boxes = boxes[_box_pos]
    box_confidences = box_confidences[_box_pos]
    box_class_probs = box_class_probs[_box_pos]
 
    class_max_score = np.max(box_class_probs, axis=-1)
    classes = np.argmax(box_class_probs, axis=-1)
    _class_pos = np.where(class_max_score >= OBJ_THRESH)
 
    boxes = boxes[_class_pos]
    classes = classes[_class_pos]
    scores = (class_max_score* box_confidences)[_class_pos]
 
    return boxes, classes, scores
 
 
def nms_boxes(boxes, scores):
    """Suppress non-maximal boxes.
    # Arguments
        boxes: ndarray, boxes of objects.
        scores: ndarray, scores of objects.
    # Returns
        keep: ndarray, index of effective boxes.
    """
    x = boxes[:, 0]
    y = boxes[:, 1]
    w = boxes[:, 2] - boxes[:, 0]
    h = boxes[:, 3] - boxes[:, 1]
 
    areas = w * h
    order = scores.argsort()[::-1]
 
    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
 
        xx1 = np.maximum(x[i], x[order[1:]])
        yy1 = np.maximum(y[i], y[order[1:]])
        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
 
        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
        inter = w1 * h1
 
        ovr = inter / (areas[i] + areas[order[1:]] - inter)
        inds = np.where(ovr <= NMS_THRESH)[0]
        order = order[inds + 1]
    keep = np.array(keep)
    return keep
 
 
def yolov5_post_process(input_data):
    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
               [59, 119], [116, 90], [156, 198], [373, 326]]
 
    boxes, classes, scores = [], [], []
    for input, mask in zip(input_data, masks):
        b, c, s = process(input, mask, anchors)
        b, c, s = filter_boxes(b, c, s)
        boxes.append(b)
        classes.append(c)
        scores.append(s)
 
    boxes = np.concatenate(boxes)
    boxes = xywh2xyxy(boxes)
    classes = np.concatenate(classes)
    scores = np.concatenate(scores)
 
    nboxes, nclasses, nscores = [], [], []
    for c in set(classes):
        inds = np.where(classes == c)
        b = boxes[inds]
        c = classes[inds]
        s = scores[inds]
 
        keep = nms_boxes(b, s)
 
        nboxes.append(b[keep])
        nclasses.append(c[keep])
        nscores.append(s[keep])
 
    if not nclasses and not nscores:
        return None, None, None
 
    boxes = np.concatenate(nboxes)
    classes = np.concatenate(nclasses)
    scores = np.concatenate(nscores)
 
    return boxes, classes, scores
 
 
def draw(image, boxes, scores, classes):
    """Draw the boxes on the image.
    # Argument:
        image: original image.
        boxes: ndarray, boxes of objects.
        classes: ndarray, classes of objects.
        scores: ndarray, scores of objects.
        all_classes: all classes name.
    """
    for box, score, cl in zip(boxes, scores, classes):
        top, left, right, bottom = box
        print('class: {}, score: {}'.format(CLASSES[cl], score))
        print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
        top = int(top)
        left = int(left)
        right = int(right)
        bottom = int(bottom)
 
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
                    (top, left - 6),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    0.6, (0, 0, 255), 2)
 
 
def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
    # Resize and pad image while meeting stride-multiple constraints
    shape = im.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)
 
    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
 
    # Compute padding
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
 
    dw /= 2  # divide padding into 2 sides
    dh /= 2
 
    if shape[::-1] != new_unpad:  # resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return im, ratio, (dw, dh)
 
def detect_white_and_circles(image0, pub):
    # 转换图像到 HSV 色彩空间
    image = image0.copy()
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
    image = cv2.GaussianBlur(image, (5,5), 0)
    # 查找掩模图像中的圆形
    circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 1, minDist=1000,param1=48, param2=57, minRadius=40, maxRadius=70)

    # 如果找到了圆形
    if circles is not None:
        circles = np.round(circles[0, :]).astype("int")  # 转换为整数
        
        for circle in circles:
            x, y, r = circle  # 提取圆形参数
            # 画圆形
            cv2.circle(image, (x, y), r, (0, 255, 255), 3)  # 绘制圆形边界
            cv2.circle(image, (x, y), 5, (0, 0, 255), 10)  # 绘制圆心
            # 在圆心旁边显示坐标和半径
            text = f"({x}, {y}), r={r}"
            cv2.putText(image, text, (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 2)
            
            # 发送圆心位置和半径信息
            msg = Yolo()
            msg.Class = str(2)
            msg.x = int(x)
            msg.y = int(y)
            msg.Confidence = 100
            pub.publish(msg)

    # 最终展示带有圆形标注的图像
    cv2.imshow("Detected Circles on White Areas", image)



if __name__ == '__main__':

    rknn = RKNNLite()

    ret = rknn.load_rknn(RKNN_MODEL)
 
 
    ret = rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0)  #使用0 1 2三个NPU核心
cap = cv2.VideoCapture(0)
count  = 0


ledcount = 0
ledflag = 0
while not rospy.is_shutdown():

    ret, img = cap.read()
    if not ret:
        break
    count+=1
    ledcount+=1
    if ledcount >= 20:
        ledcount = 0
        ledflag = 1- ledflag
        if ledflag:

            os.system("gpio write 12 0")
        else:
            os.system("gpio write 12 1")




    detect_white_and_circles(img,pub)
    if count >= 1:
        count = 0

    
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
        Real_img= np.expand_dims(img, axis=0)
        outputs = rknn.inference(inputs=[Real_img])
        input0_data = outputs[0]
        input1_data = outputs[1]
        input2_data = outputs[2]
    
        input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
        input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
        input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))
    
        input_data = list()
        input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
        input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
        input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
    
        boxes, classes, scores = yolov5_post_process(input_data)

        img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
        #findring(img_1)
        if boxes is not None:
            draw(img_1, boxes, scores, classes)
        
            for i, (cls, score) in enumerate(zip(classes, scores)):
                
                box = boxes[i]
                x_min, y_min, x_max, y_max = box

                # 计算中心坐标
                center_x = (x_min + x_max) / 2
                center_y = (y_min + y_max) / 2


                msg = Yolo()
                msg.Class = str(CLASSES[cls])
                msg.x = int(center_x)
                msg.y = int(center_y)
                msg.Confidence = score
                pub.publish(msg)

            
        
    
    #img_1 = cv2.resize(img_1, (320, 320))
        cv2.imshow("post process result", img_1)
    cv2.waitKey(1)
    rate.sleep()
 

cap.release()
 

cv2.destroyAllWindows()
rknn.release()


